A Generalized Machine Learning Model for Identifying Congenital Heart Defects (CHDs) Using ICD Codes.

Journal: Birth defects research
PMID: 39890469

Abstract

BACKGROUND: International Classification of Diseases (ICD) codes utilized for congenital heart defect (CHD) case identification in datasets have substantial false-positive (FP) rates. Incorporating machine learning (ML) algorithms following case selection by ICD codes may improve the accuracy of CHD identification, enhancing surveillance efforts.

Authors

  • Haoming Shi
    Department of Biomedical Engineering, Georgia Institute Technology, Atlanta, Georgia, USA.
  • Wendy M Book
    Department of Cardiology, School of Medicine Emory University Atlanta GA.
  • Lindsey C Ivey
    Rollins School of Public Health Emory University Atlanta GA.
  • Fred H Rodriguez
    Department of Cardiology, School of Medicine Emory University Atlanta GA.
  • Cheryl Raskind-Hood
    Department of Epidemiology, Emory University, Rollins School of Public Health, Atlanta, Georgia, USA.
  • Karrie F Downing
    National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
  • Sherry L Farr
    National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
  • Courtney E McCracken
    Center for Research and Evaluation, Kaiser Permanente Georgia, Atlanta, Georgia, USA.
  • Vinita O Leedom
    South Carolina Department of Health and Environmental Control, Columbia, South Carolina, USA.
  • Susan E Haynes
    Prisma Health Upstate, Greenville, South Carolina, USA.
  • Sandra Amouzou
    Center for Research and Evaluation, Kaiser Permanente Georgia, Atlanta, Georgia, USA.
  • Reza Sameni
    Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA.
  • Rishikesan Kamaleswaran
    Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA.

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